Data Engineer Interview Questions (With Hints)
6 questions covering behavioral, technical, and situational scenarios. Each answer hint reflects what interviewers at top companies are actually evaluating.
Behavioral Questions
Q: Describe the most complex ETL pipeline you've built. What were the hardest design decisions?
What they're looking for: They want specifics about data volume, latency requirements, idempotency handling, and error recovery strategy.
Technical Questions
Q: Design a real-time data pipeline that ingests 10 million events per day from a mobile app and makes them queryable within 30 seconds.
What they're looking for: Cover Kafka for ingestion, stream processing (Flink or Spark Streaming), partitioning strategy, and hot vs. cold storage trade-offs.
Q: How would you handle schema evolution in a data warehouse without breaking downstream consumers?
What they're looking for: Backward-compatible changes (adding nullable columns), versioning strategies, and contract testing between producers and consumers.
Q: Explain the difference between a star schema and a snowflake schema. When would you use each?
What they're looking for: Star schema favors query performance (denormalized); snowflake schema saves storage (normalized). Business users favor stars; DBA-heavy orgs favor snowflake.
Situational Questions
Q: You notice a critical data pipeline has been silently failing for 3 days and downstream reports are wrong. What do you do?
What they're looking for: Cover root cause analysis, stakeholder notification, data reconciliation, backfill strategy, and monitoring improvements to prevent recurrence.
Q: How do you ensure data quality in a pipeline that ingests data from 15 different source systems?
What they're looking for: Cover schema validation, null checks, referential integrity, great_expectations or dbt tests, alerting thresholds, and SLA enforcement.
How to Prepare
For behavioral questions, prepare 6–8 specific stories from your experience using the STAR format (Situation, Task, Action, Result). Practice answers out loud — not in your head — at least three times per question. Technical questions for Data Engineer roles require domain-specific preparation; review the skills list and be prepared to demonstrate hands-on knowledge, not just conceptual understanding.